E3S Web Conf.
Volume 120, 20192019 2nd International Conference on Green Energy and Environment Engineering (CGEEE 2019)
|Number of page(s)||5|
|Section||Ecological Management and Pollution Control|
|Published online||27 September 2019|
Machine learning algorithms for predicting air pollutants
Industrial Engineering Department, Kasetsart University, 50 Ngamwongwan Rd, Ladyao Chatuchak Bangkok, Thailand
* Corresponding author: email@example.com
An atmospheric particular matter, commonly recognized as PM, contains solid particles and liquid droplets suspending in an ambient air. A high concentration of PM is known to seriously cause adverse health effects to humans especially a small-sized particle, known as PM2.5. Not only health effects, environmental effects are also obviously observed. This work aims to estimate a likelihood of PM2.5 exceeding a pre-defined safety threshold. Multiple machine learning models are explored in this work. Particularly, classification models are implemented based on meteorological data and air pollutant features measured at different altitudes above a ground level. These features are shifted back to various time steps resulting in more insightful time-lagged features. Furthermore, a feature selection technique is implemented to specify a desirable set of important features. A re-sampling technique is also employed to address an unbalancing level of the response value in an original data set. The proposed models are evaluated on a case study whose data set is collected from an air monitoring station located in Bangkok, Thailand.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.